# Distributed Variational Bayesian Algorithms Over Sensor Networks

arXiv: https://arxiv.org/abs/2011.13600 · HTML: https://mlsanity.com/paper/2011.13600/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Distributed Variational Inference for Online Supervised Learning](https://mlsanity.com/paper/2309.02606/) (2023)
- [Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes](https://mlsanity.com/paper/2408.13689/) (2024)
- [Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features](https://mlsanity.com/paper/2201.09965/) (2022)

## Abstract (arXiv)

Distributed inference/estimation in Bayesian framework in the context of sensor networks has recently received much attention due to its broad applicability. The variational Bayesian (VB) algorithm is a technique for approximating intractable integrals arising in Bayesian inference. In this paper, we propose two novel distributed VB algorithms for general Bayesian inference problem, which can be applied to a very general class of conjugate-exponential models. In the first approach, the global natural parameters at each node are optimized using a stochastic natural gradient that utilizes the Riemannian geometry of the approximation space, followed by an information diffusion step for cooperation with the neighbors. In the second method, a constrained optimization formulation for distributed estimation is established in natural parameter space and solved by alternating direction method of multipliers (ADMM). An application of the distributed inference/estimation of a Bayesian Gaussian mixture model is then presented, to evaluate the effectiveness of the proposed algorithms. Simulations on both synthetic and real datasets demonstrate that the proposed algorithms have excellent performance, which are almost as good as the corresponding centralized VB algorithm relying on all data available in a fusion center.